"""simple docstring"""



import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )






class  snake_case__      (       unittest.TestCase       ):


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=13    ,		lowerCamelCase=7    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=99    ,		lowerCamelCase=32    ,		lowerCamelCase=5    ,		lowerCamelCase=4    ,		lowerCamelCase=37    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=512    ,		lowerCamelCase=16    ,		lowerCamelCase=2    ,		lowerCamelCase=0.02    ,		lowerCamelCase=4    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    seq_length
        __a       =    is_training
        __a       =    use_attention_mask
        __a       =    use_token_type_ids
        __a       =    use_labels
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size
        __a       =    hidden_act
        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    max_position_embeddings
        __a       =    type_vocab_size
        __a       =    type_sequence_label_size
        __a       =    initializer_range
        __a       =    num_choices


 def 						a__   ( self						):
        __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size						)

        __a       =    None
        if self.use_attention_mask:
               __a       =    random_attention_mask([self.batch_size, self.seq_length]						)

        __a       =    None
        if self.use_token_type_ids:
               __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.type_vocab_size						)

        __a       =    RobertaPreLayerNormConfig(
            vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		type_vocab_size=self.type_vocab_size    ,		is_decoder=lowerCamelCase    ,		initializer_range=self.initializer_range    ,		)

        return config, input_ids, token_type_ids, attention_mask


 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        __a ,   __a ,   __a ,   __a       =    config_and_inputs
        __a       =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
        return config, inputs_dict






 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        __a ,   __a ,   __a ,   __a       =    config_and_inputs

        __a       =    True
        __a       =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
        __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		vocab_size=2						)

        return (
            config,
            input_ids,
            token_type_ids,
            encoder_hidden_states,
            encoder_attention_mask,
        )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Dict							  =						True

 _snake_case       :					Dict							  =						(
     (
         FlaxRobertaPreLayerNormModel,
         FlaxRobertaPreLayerNormForCausalLM,
         FlaxRobertaPreLayerNormForMaskedLM,
         FlaxRobertaPreLayerNormForSequenceClassification,
         FlaxRobertaPreLayerNormForTokenClassification,
         FlaxRobertaPreLayerNormForMultipleChoice,
         FlaxRobertaPreLayerNormForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )


 def 						a__   ( self						):
        __a       =    FlaxRobertaPreLayerNormModelTester(self						)






 @slow
 def 						a__   ( self						):
        for model_class_name in self.all_model_classes:
               __a       =    model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"    ,		from_pt=lowerCamelCase						)
               __a       =    model(np.ones((1, 1)						)						)
               self.assertIsNotNone(lowerCamelCase						)






@require_flax
class  snake_case__      (       unittest.TestCase       ):


 @slow
 def 						a__   ( self						):
        __a       =    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"    ,		from_pt=lowerCamelCase						)

        __a       =    np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]    ,		dtype=jnp.intaa						)
        __a       =    model(lowerCamelCase						)[0]
        __a       =    [1, 11, 50265]
        self.assertEqual(list(output.shape						)    ,		lowerCamelCase						)
        # compare the actual values for a slice.
        __a       =    np.array(
            [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]    ,		dtype=np.floataa						)
        self.assertTrue(np.allclose(output[:, :3, :3]    ,		lowerCamelCase    ,		atol=1E-4						)						)






 @slow
 def 						a__   ( self						):
        __a       =    FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"    ,		from_pt=lowerCamelCase						)

        __a       =    np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]    ,		dtype=jnp.intaa						)
        __a       =    model(lowerCamelCase						)[0]
        # compare the actual values for a slice.
        __a       =    np.array(
            [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]    ,		dtype=np.floataa						)
        self.assertTrue(np.allclose(output[:, :3, :3]    ,		lowerCamelCase    ,		atol=1E-4						)						)

"""simple docstring"""



def 	_lowerCamelCase(	a				):
       return " ".join(
           "".join(word[::-1]				) if len(a				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(reverse_long_words("""Hey wollef sroirraw"""))
